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Short-Term Wind Turbine Blade Icing Wind Power Prediction Based on PCA-fLsm

Author

Listed:
  • Fan Cai

    (School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China
    Key Laboratory of Industrial Automation Control Technology and Application of Fujian Higher Education, Quanzhou 362700, China)

  • Yuesong Jiang

    (School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China
    School of Electronic and Information Engineering, Beihang University, Beijing 100191, China)

  • Wanqing Song

    (School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China
    School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China)

  • Kai-Hung Lu

    (School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China)

  • Tongbo Zhu

    (School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China
    Key Laboratory of Industrial Automation Control Technology and Application of Fujian Higher Education, Quanzhou 362700, China)

Abstract

To enhance the economic viability of wind energy in cold regions and ensure the safe operational management of wind farms, this paper proposes a short-term wind turbine blade icing wind power prediction method that combines principal component analysis (PCA) and fractional Lévy stable motion (fLsm). By applying supervisory control and data acquisition (SCADA) data from wind turbines experiencing icing in a mountainous area of Yunnan Province, China, the model comprehensively considers long-range dependence (LRD) and self-similar features. Adopting a combined pattern of previous-day predictions and actual measurement data, the model predicts the power under near-icing conditions, thereby enhancing the credibility and accuracy of icing forecasts. After validation and comparison with other prediction models (fBm, CNN-Attention-GRU, XGBoost), the model demonstrates a remarkable advantage in accuracy, achieving an accuracy rate and F1 score of 96.86% and 97.13%, respectively. This study proves the feasibility and wide applicability of the proposed model, providing robust data support for reducing wind turbine efficiency losses and minimizing operational risks.

Suggested Citation

  • Fan Cai & Yuesong Jiang & Wanqing Song & Kai-Hung Lu & Tongbo Zhu, 2024. "Short-Term Wind Turbine Blade Icing Wind Power Prediction Based on PCA-fLsm," Energies, MDPI, vol. 17(6), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1335-:d:1354834
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    References listed on IDEAS

    as
    1. Cheng Tao & Tao Tao & Xinjian Bai & Yongqian Liu, 2023. "Wind Turbine Blade Icing Prediction Using Focal Loss Function and CNN-Attention-GRU Algorithm," Energies, MDPI, vol. 16(15), pages 1-15, July.
    2. Tao, Tao & Liu, Yongqian & Qiao, Yanhui & Gao, Linyue & Lu, Jiaoyang & Zhang, Ce & Wang, Yu, 2021. "Wind turbine blade icing diagnosis using hybrid features and Stacked-XGBoost algorithm," Renewable Energy, Elsevier, vol. 180(C), pages 1004-1013.
    3. Bonginkosi A. Thango & Pitshou N. Bokoro, 2022. "Prediction of the Degree of Polymerization in Transformer Cellulose Insulation Using the Feedforward Backpropagation Artificial Neural Network," Energies, MDPI, vol. 15(12), pages 1-12, June.
    4. Bai, Xinjian & Tao, Tao & Gao, Linyue & Tao, Cheng & Liu, Yongqian, 2023. "Wind turbine blade icing diagnosis using RFECV-TSVM pseudo-sample processing," Renewable Energy, Elsevier, vol. 211(C), pages 412-419.
    5. Logan G. Wright & Tatsuhiro Onodera & Martin M. Stein & Tianyu Wang & Darren T. Schachter & Zoey Hu & Peter L. McMahon, 2022. "Deep physical neural networks trained with backpropagation," Nature, Nature, vol. 601(7894), pages 549-555, January.
    6. Wang, Qiang & Yi, Xian & Liu, Yu & Ren, Jinghao & Li, Weihao & Wang, Qiao & Lai, Qingren, 2020. "Simulation and analysis of wind turbine ice accretion under yaw condition via an Improved Multi-Shot Icing Computational Model," Renewable Energy, Elsevier, vol. 162(C), pages 1854-1873.
    7. Putz, Dominik & Gumhalter, Michael & Auer, Hans, 2021. "A novel approach to multi-horizon wind power forecasting based on deep neural architecture," Renewable Energy, Elsevier, vol. 178(C), pages 494-505.
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